374 件 ・ 関連度順
概要と使いどころ
Run deepsec, an AI-powered cyber-security vulnerability scanner. Activates when the user invokes /deepsec, asks to run deepsec, or wants to scan their repo, branch, or uncommitted changes for vulnerabilities.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive Windows privilege escalation methodology for offensive security engagements. Covers the full attack surface from a standard user shell to NT AUTHORITY\SYSTEM: token impersonation via SeImpersonate and SeAssignPrimaryToken privileges using JuicyPotato, PrintSpoofer, GodPotato, SweetPotato, and RoguePotato; service misconfigurations including unquoted service paths, weak service DACLs, writable service binaries, and insecure service creation permissions; AlwaysInstallElevated MSI exploitation; DLL hijacking through search order abuse, phantom DLL loading, and writable PATH directory injection; UAC bypass techniques via fodhelper.exe, eventvwr.exe, CMSTP, and environment variable manipulation; scheduled task abuse for writable task actions and new task creation; registry autorun exploitation for persistence and escalation; PrintNightmare (CVE-2021-34527) for remote and local privilege escalation; and credential harvesting from SAM database extraction, DPAPI blob decryption, LSA secret dumping, and Credential Manager enumeration. Integrates automated enumeration with WinPEAS, PowerUp, SharpUp, Seatbelt, and BeRoot. Each technique includes detection signatures and defender-side visibility for purple team operations. Maps to MITRE ATT&CK T1548 (Abuse Elevation Control Mechanism) and T1574 (Hijack Execution Flow). Designed for authorized penetration testing, red team engagements, and CTF competitions where you hold a standard user shell and need to escalate to SYSTEM or local Administrator.
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When the user wants to build an AI-powered outreach system, write cold emails, improve deliverability, or scale personalized outreach. Also use when the user mentions 'cold email,' 'cold outreach,' 'outreach automation,' 'Instantly,' 'Smartlead,' 'Clay,' 'email sequences,' 'deliverability,' 'personalization at scale,' 'reply rate,' or 'outreach stack.' This skill covers the complete AI cold outreach system from signal detection through conversion. Do NOT use for technical implementation, code review, or software architecture.
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VS-Enhanced Quantitative Design Consultant with Materials & Sampling Enhanced VS 3-Phase process: Avoids obvious experimental designs, proposes context-optimal quantitative strategies Absorbed C4 (Experimental Materials Developer) and D1 (Sampling Strategy Advisor) capabilities Use when: selecting quantitative research design, planning experimental/survey methodology, power analysis, developing materials, sampling Triggers: RCT, quasi-experimental, experimental design, survey design, power analysis, sample size, factorial design, materials, stimuli, sampling strategy
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Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
日本語の概要は準備中です。原文の説明を表示しています。
AI-powered adversarial UI testing via the browse CLI. Analyzes git diffs to test only what changed, or explores the full app to find bugs. Tests functional correctness, accessibility, responsive layout, and UX heuristics. Use when the user asks to test UI changes, QA a pull request, audit accessibility, or run exploratory testing. Supports local browser (localhost) and remote Browserbase (deployed sites).
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Use this skill whenever the agent is about to recommend or apply a change that touches DPU / NIC hardware state on a live system — mlxconfig firmware-parameter write, NIC firmware burn, BFB reflash, NIC ↔ DPU mode flip, SR-IOV or device-emulation slot enable, kernel boot-parameter change (IOMMU, hugepages, VFIO), PCIe rebind / rescan / link-state flip, or BlueField cold reboot. Wraps the change in pre-flight inventory, OOB reachability, a maintenance window, the mlxconfig cold-power-cycle rule, replica rehearsal, and rollback. Trigger even when the user does not say "hardware safety" — implicit phrasings: "flip BlueField mode over SSH", "enable SR-IOV and reboot", "burned firmware but mlxconfig shows old value", "reflashed BFB and lost representors", "reflash during business hours", "vendor says this is one-way". Refuse for general DOCA orientation (doca-public-knowledge-map), install or env debug (doca-setup), and program-side debug (doca-debug, doca-programming-guide) — those belong to other skills.
日本語の概要は準備中です。原文の説明を表示しています。
Create and audit editable scientific posters in macro-free PowerPoint (.pptx) from author-approved local content and assets. Use when the requested deliverable is a PowerPoint research/conference poster and exact physical, printer, accessibility, provenance, and package-security checks are required.
日本語の概要は準備中です。原文の説明を表示しています。
Plans sample size estimation logic, power assumptions, feasibility checks, and fallback enrollment strategies for clinical and translational study protocols.
日本語の概要は準備中です。原文の説明を表示しています。
Use for day-ahead or multi-period unit commitment problems, including thermal on/off schedules, dispatch, startup/shutdown logic, minimum up/down time, ramping, spinning reserve deliverability, renewable curtailment, operating-cost accounting, and independent feasibility checks for power-system operations schedules.
日本語の概要は準備中です。原文の説明を表示しています。
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
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Reapply and repair Windows Codex Desktop after Store upgrades, including custom provider models hidden by Statsig available_models filtering, the dependent blue-purple Power slider and its Ultra toggle, Fast Mode request/UI gates, locale i18n, plugin UI gates, Chrome/browser_use gates, Goal command gates, Windows Computer Use availability gates and plugin/runtime repair, phone remote-control pairing under third-party/API-key main app usage, Desktop dynamicTools/inputSchema thread-start schema drift, local conversation visibility recovery after model_provider switches, restored-conversation missing-cwd continuation repair, ASAR integrity repair, signing/installing patched MSIX packages, SDK cleanup, Fast Mode wire verification, local plugin marketplace registration, and optional custom model_instructions_file setup (the bundled system prompt, also called 听话水).
日本語の概要は準備中です。原文の説明を表示しています。
Estimates Fabric capacity cost before a migration by profiling Spark, SQL, Power BI, and Real-Time workloads, then recommending SKU size and comparing Reserved, Pay-As-You-Go, and Autoscale billing. Use only for capacity sizing and pricing questions, not for general migration or connectivity work.
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Manages Fabric IQ Ontology items, including entity and relationship types, data bindings, and definition updates, plus schema, lineage, grounding, and graph-walk exploration. Use for ontology modelling and traversal. For natural-language questions over a Power BI report use fabriciq.
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SVG generation via DAX measures and extension measures with ImageUrl data category for inline visualizations in PBIR reports, including ready templates for matrix and table cells, new card images and per-button images on the new button slicer. Automatically invoke when the user mentions "SVG visual", "DAX sparkline", "SVG measure", "inline graphics with DAX", "ImageUrl data category", "extension measure", or asks to create any DAX-generated chart (progress bars, bullet charts, KPI indicators, data bars, gauges, donut charts, lollipop charts, dumbbell charts, status pills, overlapping bars, boxplots, IBCS bars, jitter plots, box-and-whisker charts).
日本語の概要は準備中です。原文の説明を表示しています。
Audit and improve an entire agentic-development setup, including skills, context, memory, tools, models, permissions, hooks, workflow habits, and secrets hygiene. Invoke for setup reviews, workflow grading, Goblin Mode, or questions about what to improve. Supports shallow, deep, ultra, and yolo modes. Use domain-specific skills for a single model, report, or tenant.
日本語の概要は準備中です。原文の説明を表示しています。
Execute arbitrary Python or PySpark code on Fabric Spark compute without creating a notebook artifact; ephemeral Livy sessions with full Delta table access. Automatically invoke when the user asks to "run PySpark in Fabric", "create a Livy session", "execute Python on Fabric compute", "run Spark without a notebook", "submit code to Fabric", "ephemeral Spark execution", "run ETL in Fabric".
日本語の概要は準備中です。原文の説明を表示しています。
Reviews the design of an existing Power Pages site - a live URL or a local project folder - and recommends what to change, without modifying anything. Captures up to eight key pages at desktop and mobile, scores them against the plugin's design rubric (first impression, hierarchy, narrative, copy, trust, brand, responsive and accessible design, motion, detail), and returns prioritized, concrete recommendations. Use when the user wants a design review, critique, or audit of a site, asks how to make an existing site look better, more polished, or more impressive, or asks why it looks generic or dated.
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Audits existing table permissions on a Power Pages site by analyzing them against site code and Dataverse metadata, then GRADES the configuration with deterministic 1–5 scores across 3 security/usability/correctness categories (9 dimensions). Detection is anchored by a recurring-issue catalog so findings and severities stay consistent run-to-run. Generates an HTML report with an at-a-glance verdict and per-category scores in the Overview plus a single list of **major/minor** issues (each major issue labelled permissions-only or upstream-caused with a short justification), and suggested fixes. Use when the user wants to review, verify, check, score, or grade table permissions for security issues.
日本語の概要は準備中です。原文の説明を表示しています。
Read the approved PRD.md and generate the native sources for a third-party PAM control (the compiled `.ppmplugin` track) — iOS Obj-C `<Pascal>Module` plus optional system-frameworks podspec, Android Kotlin `<Pascal>Module` with build.gradle, AndroidManifest and ReactPackage, a dev-only private package.json (react + react-native devDeps for the builds), and the committed `./manifest.json` dispatch contract the PCF and build stage both read. No TypeScript INativeExtension layer — the contract is the manifest plus the native modules' dispatch surface. Emits the layout in shared/repo-layout.md and generates substantially complete native code (compiled later by /build-android-binary and /build-ios-binary, not here). Local only — writes files, runs no git and touches no remote or feed. PCF is generated by /generate-pcf-companion; the bundle is built by /generate-ppmplugin.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes the user's business problem and recommends the right backend integration approach — Web API, AI Web API (generative summaries / grounded search), Server Logic, Cloud Flows, or a combination — for a Power Pages site, then routes to the appropriate specialized skill. Use when the user wants to add backend integration, connect to data, add AI summaries, or needs help deciding which backend approach to use.
日本語の概要は準備中です。原文の説明を表示しています。
Validate a third-party control repo across four automated layers plus one printed manual recipe. Layer 1 asserts native-source structure (Android getName() and iOS +moduleName to manifest nativeModule; @ReactMethod / RCT_EXPORT_METHOD to methods; no @ReactModule) plus load/init readiness (ReactPackage public no-arg constructor, iOS [cls new] no-arg init, requiresMainQueueSetup NO, non-throwing eager construction), so launch-time crashes surface before any build. Layer 2 validates the committed `./manifest.json` against the ppmplugin-format rules. Layer 3 asserts request/response/error-code agreement across native and PCF. Layer 4 compiles the PCF (auto-skipped if absent). Layer 5 prints a device end-to-end recipe. Native compile belongs to /build-android-binary and /build-ios-binary — this is the cheap structural pre-flight before those slow builds. Reports pass/fail per layer with a fix hint and updates .extension-state.md.
日本語の概要は準備中です。原文の説明を表示しています。
Internal mobile-app workflow read and executed only by /setup-offline-profile to enable Dataverse table prerequisites for a Mobile Offline Profile.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to design or redesign the Dataverse schema and connector plan for an existing mobile app, or has an ER diagram (image, Mermaid, or text) to apply. Skip when the user is creating a brand-new app — /create-mobile-app handles the data model inline.
日本語の概要は準備中です。原文の説明を表示しています。